Adaptive lane marker detection for a vehicular vision system

ABSTRACT

A method for determining lane markers includes providing a camera at a vehicle so as to have a field of view exterior of the vehicle. An image processor processes a frame of image data captured by the camera to determine intensity gradient information of captured image data and to determine lane markers. Contrast values at both sides of a center region of the determined lane marker are determined via processing of the intensity gradient information. An angle of the determined lane marker relative to the direction of travel of the vehicle is determined responsive to the determined contrast values. Processing of a subsequent frame of captured image data is adjusted responsive to the determined angle of the determined lane marker relative to the direction of travel of the vehicle.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 14/969,526, filed Dec. 15, 2015, now U.S. Pat. No. 9,946,940, which claims the filing benefits of U.S. provisional application Ser. No. 62/093,742, filed Dec. 18, 2014, which is hereby incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to a vehicle vision system for a vehicle and, more particularly, to a vehicle vision system that utilizes one or more cameras at a vehicle.

BACKGROUND OF THE INVENTION

Use of imaging sensors in vehicle imaging systems is common and known. Examples of such known systems are described in U.S. Pat. Nos. 5,949,331; 5,670,935 and/or 5,550,677, which are hereby incorporated herein by reference in their entireties.

SUMMARY OF THE INVENTION

The present invention provides a collision avoidance system or vision system or imaging system for a vehicle that utilizes one or more cameras (preferably one or more CMOS cameras) to capture image data representative of images exterior of the vehicle, and provides a way of extracting line segments from captured image data for road marking detection. The method of the present invention extracts line segments on road surfaces for the purpose of identifying either the lane markings or the park line markings.

Typically, the lane markings are extracted using gradient processing followed by Hough transform or via the use of training. Both of these processes tend to be computationally expensive and have limitations on the kind of markings which could be extracted without generalization (straight vs curved, black vs white, and/or the like). The method of the present invention addresses these concerns by proposing a unified method which can extract black as well as white lines, where the lines could be either curved or straight.

Given that the system uses a wide angle field of view camera that captures images or image data for processing, even straight lines could look either curved or straight depending on where in the field of view and image they lie. Thus, a harmonized approach which can extract curvature as well as straight edges simultaneously is advantageous.

Computationally also, the method of the present invention is cheaper and would yield to easier implementation on the hardware, thus making it favorable for embedded applications.

These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a plan view of a vehicle with a vision system that incorporates cameras in accordance with the present invention;

FIG. 2 shows topology profiles of white and black band lines on a road;

FIG. 3 shows the topology profiles of FIG. 2, and shows equations for measuring the contrasts at both sides of the band lines;

FIG. 4 shows use of the equations of FIG. 3 applied over all angles to obtain a maximum value;

FIG. 5 is a table showing the properties of the edges of the band lines;

FIG. 6 shows a library of sets of three pixels for typical masks obtained for some angles;

FIG. 7 shows how a mask is rotated to extract pixels to define coordinates of pixels of a mask;

FIG. 8 shows application using typical angles and two kinds of images;

FIG. 9 shows top view images using a mask size Xs=9 and Ys=1;

FIG. 10 shows top view images using a mask size Xs=2 and Ys−1;

FIG. 11 shows corrected top view images of FIG. 9;

FIG. 12 shows corrected top view images of FIG. 10;

FIGS. 13 and 14 show images after applying the equation of FIG. 4 and taking a maximum contrast;

FIGS. 15-17 show images of image contrast and image from contrast;

FIG. 18 shows a synthetic image composed of different lines and shapes;

FIG. 19 shows use of an adaptive filiformity measure to detect lines in the synthetic image of FIG. 18;

FIG. 20 shows examples of elimination of small edges;

FIG. 21 shows application of the bounding boxes of detected edges;

FIG. 22 shows how the edges are regrouped to form lines;

FIG. 23 shows how groups of edges are approximated by lines using linear regression; and

FIG. 24 shows how the regrouped edges are at lines of the captured image.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

A vehicle vision system and/or driver assist system and/or object detection system and/or alert system operates to capture images exterior of the vehicle and may process the captured image data to display images and to detect objects at or near the vehicle and in the predicted path of the vehicle, such as to assist a driver of the vehicle in maneuvering the vehicle in a rearward direction. The vision system includes an image processor or image processing system that is operable to receive image data from one or more cameras and provide an output to a display device for displaying images representative of the captured image data. Optionally, the vision system may provide a top down or bird's eye or surround view display and may provide a displayed image that is representative of the subject vehicle, and optionally with the displayed image being customized to at least partially correspond to the actual subject vehicle.

Referring now to the drawings and the illustrative embodiments depicted therein, a vehicle 10 includes an imaging system or vision system 12 that includes at least one exterior facing imaging sensor or camera, such as a rearward facing imaging sensor or camera 14 a (and the system may optionally include multiple exterior facing imaging sensors or cameras, such as a forwardly facing camera 14 b at the front (or at the windshield) of the vehicle, and a sidewardly/rearwardly facing camera 14 c, 14 d at respective sides of the vehicle), which captures images exterior of the vehicle, with the camera having a lens for focusing images at or onto an imaging array or imaging plane or imager of the camera (FIG. 1). The vision system 12 includes a control or electronic control unit (ECU) or processor 18 that is operable to process image data captured by the cameras and may provide displayed images at a display device 16 for viewing by the driver of the vehicle (although shown in FIG. 1 as being part of or incorporated in or at an interior rearview mirror assembly 20 of the vehicle, the control and/or the display device may be disposed elsewhere at or in the vehicle). The data transfer or signal communication from the camera to the ECU may comprise any suitable data or communication link, such as a vehicle network bus or the like of the equipped vehicle.

The present invention provides a lane detection system that processes image data captured by a camera of a vehicle, such as a forward viewing camera that captures image data of the area and road ahead of the vehicle. The system processes the captured image data to extract lane marker or road marker information, and may be operable to detect straight and curved road markers (such as may be encountered along a curve in the road), and may be operable to detect different color or different intensity markers, such as by utilizing the processing system and algorithms and equations and means as described in the attached figures.

The system determines contrasts at both sides of a center point or region of a detected marker or line or gradient at the road surface, and using those contrast values over a plurality of angles or all angles, determines the maximum value over any orientation. The system may utilize a table or database of values for the central, left and right neighborhood for typical angles of a segment, and may determine the angle of the detected segment by matching the determined values with the database values. The system can focus on one or more main directions and may extract lines related to that predefined direction, and/or the system may consider a plurality of directions and extract curved or curvy or serpentine lines.

The method or system of the present invention is operable to process captured image data of a road ahead of a vehicle to extract information used for detecting road markings or lane markings on the road ahead of vehicle. The line extraction is implemented within the actual LDW framework, replacing some modules like the Prewitt, the Hough transform, the Normalized matching model and/or the like. The processor may have a speed that lies in [14, 20] fps in debug mode depending on the implementation. The system may not make an objective comparison, since some modules are still missing, like the line stabilizer, the training and/or the like. The method or system of the present invention is more suitable to extract curved or curvy lines than other methods or systems using Hough transform. The method or system of the present invention also has the potential to focalize on white only lines, black only lines, or both kinds of lines.

As shown in FIG. 2, the brightness profile of a white band is different than a black band line. In FIG. 3, a filiformity measure is applied to the profiles to measure the contrasts (w_(p), b_(p)) in both sides, left and right side of a point p. In two dimensional (2D) images, the measures w_(p), b_(p) are taken over all angles to acquire the maximum value over a plurality of orientations or any orientation (see equation II in FIG. 4). The measured contrasts have different properties depending on the line (see FIG. 5).

A dll library may provide the three sets of pixels for central, right and left neighborhoods around each point of the image, and that may be used to compute the white line contrast value w_(p). FIG. 6 shows some typical masks obtained for typical angles. As shown in FIG. 7, the library function takes three arguments: (1) alpha, the angle of the line profile, (2) mskX, the size of the mask in the x direction (along the alpha direction), and (3) mskY, the size along the y direction (perpendicular to the alpha direction). The results are used to obtain contrast images from top view images and corrected images (see FIGS. 8-17).

As an example, a synthetic image may be used that is composed of different lines having different thicknesses and different shapes (see FIG. 18). To detect the lines in the synthetic image, an adaptive filiformity measure may be used which determines only vertical lines. In processing edgels, after extracting pixels belonging to the rightmost lobe in the histogram, objects defined as sets of connected pixels are detected and labeled (using an algorithm and implementation), and objects with certain defaults are disregarded, such as objects having less than a certain amount of pixels, or objects where their bounding boxes are not that of an expected filiform object (such as the width and the height of the box have close values), or objects where the direction of the bounding box does not correspond to the expected direction that was fixed, or the like. The remaining edgels are set in groups to define lines that were being looked for in the context of LDW. An example of elimination of small edges is shown in FIG. 20.

The system may process data to determine bounding boxes of edgels (see FIG. 21). As shown in FIG. 22, the direction of the lines on the road are derived by regrouping the edgels to form lines. The system approximates a group of edges by lines using linear regression considering pixels of all edges of the group (see FIG. 23). As shown in FIG. 24, three groups of lines may be determined, with each group of boxes in its own color (e.g., magenta on the left line, yellow on the middle line and blue on the right line). If no intersection of the lines is determined within the two vertical black lines, the lines may be shown as green (otherwise, if a line is determined to intersect one of the boundary vertical black lines, that region may be shown in red).

The camera or sensor may comprise any suitable camera or sensor. Optionally, the camera may comprise a “smart camera” that includes the imaging sensor array and associated circuitry and image processing circuitry and electrical connectors and the like as part of a camera module, such as by utilizing aspects of the vision systems described in International Publication Nos. WO 2013/081984 and/or WO 2013/081985, which are hereby incorporated herein by reference in their entireties.

The system includes an image processor operable to process image data captured by the camera or cameras, such as for detecting objects or other vehicles or pedestrians or the like in the field of view of one or more of the cameras. For example, the image processor may comprise an EyeQ2 or EyeQ3 image processing chip available from Mobileye Vision Technologies Ltd. of Jerusalem, Israel, and may include object detection software (such as the types described in U.S. Pat. Nos. 7,855,755; 7,720,580 and/or 7,038,577, which are hereby incorporated herein by reference in their entireties), and may analyze image data to detect vehicles and/or other objects. Responsive to such image processing, and when an object or other vehicle is detected, the system may generate an alert to the driver of the vehicle and/or may generate an overlay at the displayed image to highlight or enhance display of the detected object or vehicle, in order to enhance the driver's awareness of the detected object or vehicle or hazardous condition during a driving maneuver of the equipped vehicle.

The vehicle may include any type of sensor or sensors, such as imaging sensors or radar sensors or lidar sensors or ladar sensors or ultrasonic sensors or the like. The imaging sensor or camera may capture image data for image processing and may comprise any suitable camera or sensing device, such as, for example, a two dimensional array of a plurality of photosensor elements arranged in at least 640 columns and 480 rows (at least a 640×480 imaging array, such as a megapixel imaging array or the like), with a respective lens focusing images onto respective portions of the array. The photosensor array may comprise a plurality of photosensor elements arranged in a photosensor array having rows and columns. Preferably, the imaging array has at least 300,000 photosensor elements or pixels, more preferably at least 500,000 photosensor elements or pixels and more preferably at least 1 million photosensor elements or pixels. The imaging array may capture color image data, such as via spectral filtering at the array, such as via an RGB (red, green and blue) filter or via a red/red complement filter or such as via an RCC (red, clear, clear) filter or the like. The logic and control circuit of the imaging sensor may function in any known manner, and the image processing and algorithmic processing may comprise any suitable means for processing the images and/or image data.

For example, the vision system and/or processing and/or camera and/or circuitry may utilize aspects described in U.S. Pat. Nos. 7,005,974; 5,760,962; 5,877,897; 5,796,094; 5,949,331; 6,222,447; 6,302,545; 6,396,397; 6,498,620; 6,523,964; 6,611,202; 6,201,642; 6,690,268; 6,717,610; 6,757,109; 6,802,617; 6,806,452; 6,822,563; 6,891,563; 6,946,978; 7,859,565; 5,550,677; 5,670,935; 6,636,258; 7,145,519; 7,161,616; 7,230,640; 7,248,283; 7,295,229; 7,301,466; 7,592,928; 7,881,496; 7,720,580; 7,038,577; 6,882,287; 5,929,786 and/or 5,786,772, which are all hereby incorporated herein by reference in their entireties. The system may communicate with other communication systems via any suitable means, such as by utilizing aspects of the systems described in International Publication Nos. WO/2010/144900; WO 2013/043661 and/or WO 2013/081985, and/or U.S. Pat. No. 9,126,525, which are hereby incorporated herein by reference in their entireties.

The imaging device and control and image processor and any associated illumination source, if applicable, may comprise any suitable components, and may utilize aspects of the cameras and vision systems described in U.S. Pat. Nos. 5,550,677; 5,877,897; 6,498,620; 5,670,935; 5,796,094; 6,396,397; 6,806,452; 6,690,268; 7,005,974; 7,937,667; 7,123,168; 7,004,606; 6,946,978; 7,038,577; 6,353,392; 6,320,176; 6,313,454 and/or 6,824,281, and/or International Publication Nos. WO 2010/099416; WO 2011/028686 and/or WO 2013/016409, and/or U.S. Pat. Publication No. US 2010-0020170, which are all hereby incorporated herein by reference in their entireties. The camera or cameras may comprise any suitable cameras or imaging sensors or camera modules, and may utilize aspects of the cameras or sensors described in U.S. Publication No. US-2009-0244361 and/or U.S. Pat. Nos. 8,542,451; 7,965,336 and/or 7,480,149, which are hereby incorporated herein by reference in their entireties. The imaging array sensor may comprise any suitable sensor, and may utilize various imaging sensors or imaging array sensors or cameras or the like, such as a CMOS imaging array sensor, a CCD sensor or other sensors or the like, such as the types described in U.S. Pat. Nos. 5,550,677; 5,670,935; 5,760,962; 5,715,093; 5,877,897; 6,922,292; 6,757,109; 6,717,610; 6,590,719; 6,201,642; 6,498,620; 5,796,094; 6,097,023; 6,320,176; 6,559,435; 6,831,261; 6,806,452; 6,396,397; 6,822,563; 6,946,978; 7,339,149; 7,038,577; 7,004,606; 7,720,580 and/or 7,965,336, and/or International Publication Nos. WO/2009/036176 and/or WO/2009/046268, which are all hereby incorporated herein by reference in their entireties.

The camera module and circuit chip or board and imaging sensor may be implemented and operated in connection with various vehicular vision-based systems, and/or may be operable utilizing the principles of such other vehicular systems, such as a vehicle headlamp control system, such as the type disclosed in U.S. Pat. Nos. 5,796,094; 6,097,023; 6,320,176; 6,559,435; 6,831,261; 7,004,606; 7,339,149 and/or 7,526,103, which are all hereby incorporated herein by reference in their entireties, a rain sensor, such as the types disclosed in commonly assigned U.S. Pat. Nos. 6,353,392; 6,313,454; 6,320,176 and/or 7,480,149, which are hereby incorporated herein by reference in their entireties, a vehicle vision system, such as a forwardly, sidewardly or rearwardly directed vehicle vision system utilizing principles disclosed in U.S. Pat. Nos. 5,550,677; 5,670,935; 5,760,962; 5,877,897; 5,949,331; 6,222,447; 6,302,545; 6,396,397; 6,498,620; 6,523,964; 6,611,202; 6,201,642; 6,690,268; 6,717,610; 6,757,109; 6,802,617; 6,806,452; 6,822,563; 6,891,563; 6,946,978 and/or 7,859,565, which are all hereby incorporated herein by reference in their entireties, a trailer hitching aid or tow check system, such as the type disclosed in U.S. Pat. No. 7,005,974, which is hereby incorporated herein by reference in its entirety, a reverse or sideward imaging system, such as for a lane change assistance system or lane departure warning system or for a blind spot or object detection system, such as imaging or detection systems of the types disclosed in U.S. Pat. Nos. 7,881,496; 7,720,580; 7,038,577; 5,929,786 and/or 5,786,772, which are hereby incorporated herein by reference in their entireties, a video device for internal cabin surveillance and/or video telephone function, such as disclosed in U.S. Pat. Nos. 5,760,962; 5,877,897; 6,690,268 and/or 7,370,983, and/or U.S. Publication No. US-2006-0050018, which are hereby incorporated herein by reference in their entireties, a traffic sign recognition system, a system for determining a distance to a leading or trailing vehicle or object, such as a system utilizing the principles disclosed in U.S. Pat. Nos. 6,396,397 and/or 7,123,168, which are hereby incorporated herein by reference in their entireties, and/or the like.

Optionally, the circuit board or chip may include circuitry for the imaging array sensor and or other electronic accessories or features, such as by utilizing compass-on-a-chip or EC driver-on-a-chip technology and aspects such as described in U.S. Pat. Nos. 7,255,451 and/or 7,480,149 and/or U.S. Publication Nos. US-2006-0061008 and/or US-2010-0097469, which are hereby incorporated herein by reference in their entireties.

Optionally, the vision system may include a display for displaying images captured by one or more of the imaging sensors for viewing by the driver of the vehicle while the driver is normally operating the vehicle. Optionally, for example, the vision system may include a video display device disposed at or in the interior rearview mirror assembly of the vehicle, such as by utilizing aspects of the video mirror display systems described in U.S. Pat. No. 6,690,268 and/or U.S. Publication No. US-2012-0162427, which are hereby incorporated herein by reference in their entireties. The video mirror display may comprise any suitable devices and systems and optionally may utilize aspects of the compass display systems described in U.S. Pat. Nos. 7,370,983; 7,329,013; 7,308,341; 7,289,037; 7,249,860; 7,004,593; 4,546,551; 5,699,044; 4,953,305; 5,576,687; 5,632,092; 5,677,851; 5,708,410; 5,737,226; 5,802,727; 5,878,370; 6,087,953; 6,173,508; 6,222,460; 6,513,252 and/or 6,642,851, and/or European patent application, published Oct. 11, 2000 under Publication No. EP 0 1043566, and/or U.S. Publication No. US-2006-0061008, which are all hereby incorporated herein by reference in their entireties. Optionally, the video mirror display screen or device may be operable to display images captured by a rearward viewing camera of the vehicle during a reversing maneuver of the vehicle (such as responsive to the vehicle gear actuator being placed in a reverse gear position or the like) to assist the driver in backing up the vehicle, and optionally may be operable to display the compass heading or directional heading character or icon when the vehicle is not undertaking a reversing maneuver, such as when the vehicle is being driven in a forward direction along a road (such as by utilizing aspects of the display system described in International Publication No. WO 2012/051500, which is hereby incorporated herein by reference in its entirety).

Optionally, the vision system (utilizing the forward facing camera and a rearward facing camera and other cameras disposed at the vehicle with exterior fields of view) may be part of or may provide a display of a top-down view or birds-eye view system of the vehicle or a surround view at the vehicle, such as by utilizing aspects of the vision systems described in International Publication Nos. WO 2010/099416; WO 2011/028686; WO 2012/075250; WO 2013/019795; WO 2012/075250; WO 2012/145822; WO 2013/081985; WO 2013/086249 and/or WO 2013/109869, which are hereby incorporated herein by reference in their entireties.

Changes and modifications in the specifically described embodiments can be carried out without departing from the principles of the invention, which is intended to be limited only by the scope of the appended claims, as interpreted according to the principles of patent law including the doctrine of equivalents. 

1. A method for determining lane markers via a vision system of a vehicle, said method comprising: providing a camera at a vehicle so as to have a field of view forward and exterior of the vehicle, wherein the field of view encompasses a road along which the vehicle is traveling, and wherein the camera comprises a pixelated imaging array having a plurality of photosensing elements; capturing frames of image data via the camera; providing a processor for processing frames of image data captured by the camera; determining, via processing by the processor of a frame of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, intensity gradient information of the frame of captured image data; determining, via processing by the processor of the frame of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, and responsive at least in part to the determined intensity gradient information of the frame of captured image data, a lane marker of the road along which the vehicle is traveling; determining, via processing by the processor of intensity gradient information of the frame of captured image data, contrast values at both sides of a center region of the determined lane marker; determining, at least in part responsive to the determined contrast values, an angle of the determined lane marker relative to the direction of travel of the vehicle; and adapting processing by the processor of a subsequent frame of captured image data responsive to the determined angle of the determined lane marker relative to the direction of travel of the vehicle.
 2. The method of claim 1, wherein determining the angle of the determined lane marker relative to the direction of travel of the vehicle is based at least in part on comparing the determined contrast values with contrast values stored in memory.
 3. The method of claim 1, comprising utilizing a database of contrast values for central, left and right regions for typical angles of a lane marker relative to the direction of travel of the vehicle, and comparing the determined contrast values with database contrast values to determine the angle of the determined lane marker relative to the direction of travel of the vehicle.
 4. The method of claim 3, wherein the database provides data for central, right and left regions around the determined lane marker.
 5. The method of claim 1, wherein determining the lane marker is based at least in part on determining segments on the road along which the vehicle is traveling.
 6. The method of claim 5, comprising grouping the segments to determine the angle of the determined lane marker relative to the direction of travel of the vehicle.
 7. The method of claim 6, wherein determining segments is via processing by the processor of captured image data.
 8. The method of claim 7, comprising deriving a direction of lane markers on the road by grouping determined segments.
 9. The method of claim 8, comprising approximating a direction of a group of determined segments using linear regression.
 10. The method of claim 7, comprising grouping, responsive to processing of the frame of captured image data, determined segments within respective bounding boxes that encompass determined points along the determined segments.
 11. The method of claim 10, wherein determining the angle of the determined lane marker relative to the direction of travel of the vehicle comprises determining, responsive to grouping determined segments, the angle of the determined lane marker at least in part responsive to a determination that a gap between centers of adjacent bounding boxes is less than a width of the larger of both bounding boxes.
 12. The method of claim 1, wherein the camera comprises a wide angle lens and wherein said camera has a wide angle field of view forward and exterior of the vehicle.
 13. A method for determining lane markers via a vision system of a vehicle, said method comprising: providing a camera at a vehicle so as to have a field of view forward and exterior of the vehicle, wherein the field of view encompasses a road along which the vehicle is traveling, and wherein the camera comprises a pixelated imaging array having a plurality of photosensing elements, and wherein the camera comprises a wide angle lens and, when provided at the vehicle, has a wide angle field of view forward and exterior of the vehicle; capturing frames of image data via the camera; providing a processor for processing frames of image data captured by the camera; determining, via processing by the processor of a frame of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, intensity gradient information of the frame of captured image data; determining, via processing by the processor of the frame of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, and responsive at least in part to the determined intensity gradient information of the frame of captured image data, a plurality of lane markers of the road along which the vehicle is traveling; determining, via processing by the processor of intensity gradient information of the frame of captured image data, contrast values at both sides of a center region of the determined lane markers; determining a maximum contrast value over a plurality of orientations of the determined lane markers relative to the direction of travel of the vehicle by using determined contrast values over a plurality of angles of the determined lane markers relative to the direction of travel of the vehicle; determining an angle of a determined lane marker of the determined lane markers relative to the direction of travel of the vehicle by comparing, utilizing a database of contrast values for central, left and right regions for typical angles of a lane marker relative to the direction of travel of the vehicle, the determined contrast values with database contrast values; and adapting processing by the processor of a subsequent frame of captured image data responsive to the determined angle of the determined lane marker relative to the direction of travel of the vehicle.
 14. The method of claim 13, wherein the database provides data for central, right and left regions around the determined lane marker.
 15. The method of claim 13, wherein determining the lane markers is based at least in part on determining segments on the road along which the vehicle is traveling, and wherein determining the angle of the determined lane markers relative to the direction of travel of the vehicle is based at least in part on grouping of the determined segments.
 16. The method of claim 15, comprising approximating a direction of a group of determined segments using linear regression.
 17. A method for determining lane markers via a vision system of a vehicle, said method comprising: providing a camera at a vehicle so as to have a field of view forward and exterior of the vehicle, wherein the field of view encompasses a road along which the vehicle is traveling, and wherein the camera comprises a pixelated imaging array having a plurality of photosensing elements, and wherein the camera comprises a wide angle lens and, when provided at the vehicle, has a wide angle field of view forward and exterior of the vehicle; capturing frames of image data via the camera; providing a processor for processing frames of image data captured by the camera; determining, via processing by the processor of frames of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, intensity gradient information of the frame of captured image data; determining, via processing by the processor of the frame of image data captured by the camera with the camera disposed at the vehicle and with the vehicle traveling along the road, and responsive at least in part to the determined intensity gradient information of the frame of captured image data, line segments on the road along which the vehicle is traveling; determining, via processing by the processor of intensity gradient information of the frame of captured image data, contrast values at both sides of a center region of the determined line segments; determining lane markers via processing by the processor of the frame of captured image data and responsive to the determined contrast values at both sides of the center region of the determined line segments; deriving a direction of the determined lane markers on the road by grouping determined line segments; and adapting processing by the processor of a subsequent frame of captured image data responsive to the derived direction of the determined lane markers.
 18. The method of claim 17, comprising approximating a direction of a group of line segments using linear regression.
 19. The method of claim 17, comprising grouping, responsive to processing of the frame of captured image data, determined line segments within respective bounding boxes that encompass determined points along the determined line segments.
 20. The method of claim 19, wherein deriving the direction of the determined lane markers comprises deriving, responsive to grouping determined line segments, the direction of the determined lane markers responsive at least in part to a determination that a gap between centers of adjacent bounding boxes is less than a width of the larger of both bounding boxes. 